What Building ContextLens Taught Me About Context-Aware Systems

A few weeks ago, I set out to build a small portfolio project: a Streamlit app that could take any tabular dataset, understand something about its structure, and give honest guidance on how to model it. I called it ContextLens . I expected it to be a practical exercise in Python, machine learning, and deployment. What I didn't expect was how closely it would connect with the same questions I work with every day in my PhD research on context-aware intelligent systems. The problem I started with Most introductory machine-learning tutorials follow a familiar sequence: Load a CSV. Choose a model. Train it. Check the accuracy. What often gets skipped is the layer of judgment that should come before any of that: Is this actually a classification problem or a regression problem? Is the target so imbalanced that accuracy becomes misleading? Is that "ID" column secretly leaking the answer into your model? Are there duplicate rows, missing values, high-cardinality categories, or too many features for the number of available observations? Experienced practitioners make these judgments almost automatically. But that reasoning usually remains invisible—it sits in someone's head rather than inside the system, where another person can inspect it. ContextLens is my attempt to make that layer visible. Upload a dataset, and it profiles the data, flags structural risks—missingness, duplicate rows, likely identifier columns, class imbalance, and high-dimensional settings—and adapts its evaluation guidance to what it finds before training a single model. The point is not simply to train a model. The point is to ask whether the modelling process makes sense in the first place. Why I call it "context-aware" rather than "AI-powered" I was deliberate about this distinction, just as I have been throughout my PhD work, and it turned out to be the most important design decision in the whole project. ContextLens does not claim to be intelligent in the way a human expert is. It does not hide its